Abstract

In the paper presented at UKIERI 2009 Workshop, an Extended Minimum Resource Allocation Neural Network (EMRANN) based controller was designed for auto-landing of high performance fighter aircraft subjected to severe wind and ineffective control surfaces due to stuck actuators. The performance of EMRANN was compared with a classical Baseline Trajectory Following Controller (BTFC). Under certain failure conditions, it was found that the BTFC alone could not meet the required performance for landing whereas EMRANN augmentation considerably improved the ability of BTFC to handle large faults while meeting the desired flight path and stringent touchdown conditions for auto-landing. This paper presents the design and implementation of a fuzzy controller named SAFIS (sequential adaptive fuzzy inference system). SAFIS is functionally equivalent to EMRANN. The performance of SAFIS is found to be identical or slightly better than EMRANN and also it requires less number of rules as compared to number of neurons for EMRANN